Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	122,361
2	South Dakota	113,764
3	Iowa	90,760
4	Wisconsin	90,661
5	Utah	89,094
6	Nebraska	87,872
7	Rhode Island	87,513
8	Tennessee	87,448
9	Idaho	80,485
10	Kansas	80,454
11	Indiana	79,122
12	Wyoming	78,179
13	Illinois	77,925
14	Arkansas	77,798
15	Oklahoma	77,526
16	Montana	77,427
17	Arizona	77,148
18	Alabama	76,296
19	Nevada	75,754
20	Mississippi	75,156
21	Minnesota	75,137
22	New Mexico	70,256
23	Missouri	70,020
24	Louisiana	69,304
25	Alaska	65,764
26	Florida	64,098
27	Georgia	63,372
28	Texas	63,222
29	South Carolina	63,214
30	Kentucky	63,141
31	California	62,627
32	Ohio	62,230
33	Delaware	61,958
34	Colorado	59,950
35	Massachusetts	56,244
36	New Jersey	55,880
37	Connecticut	54,591
38	North Carolina	54,539
39	Michigan	54,243
40	New York	53,223
41	Pennsylvania	52,395
42	West Virginia	51,271
43	Maryland	47,632
44	Virginia	43,059
45	District of Columbia	42,372
46	Puerto Rico	35,200
47	New Hampshire	34,807
48	Washington	34,318
49	Oregon	28,089
50	Maine	19,318
51	Hawaii	15,603
52	Vermont	12,617

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,497
2	Arizona	1,432
3	Oklahoma	1,333
4	Tennessee	1,201
5	South Carolina	1,163
6	California	1,086
7	Kentucky	942
8	Utah	937
9	North Carolina	891
10	Connecticut	834
11	Florida	828
12	New Hampshire	808
13	Delaware	776
14	Ohio	771
15	West Virginia	756
16	Nevada	680
17	New York	673
18	Alaska	624
19	Texas	611
20	Massachusetts	603
21	Mississippi	592
22	Indiana	590
23	Arkansas	589
24	Alabama	567
25	Georgia	561
26	South Dakota	557
27	Illinois	556
28	New Mexico	504
29	Virginia	498
30	Louisiana	495
31	Minnesota	495
32	Wyoming	482
33	Michigan	478
34	Missouri	463
35	Pennsylvania	463
36	Kansas	462
37	Idaho	456
38	Maine	438
39	New Jersey	429
40	Washington	426
41	Maryland	418
42	Colorado	398
43	Nebraska	375
44	Iowa	356
45	Montana	328
46	District of Columbia	307
47	Wisconsin	306
48	Vermont	246
49	Oregon	244
50	North Dakota	205
51	Puerto Rico	182
52	Hawaii	83

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,166
2	New York	1,961
3	Massachusetts	1,838
4	Rhode Island	1,751
5	Connecticut	1,730
6	North Dakota	1,730
7	South Dakota	1,710
8	Mississippi	1,641
9	Louisiana	1,631
10	Illinois	1,453
11	Michigan	1,339
12	Pennsylvania	1,280
13	Iowa	1,265
14	Indiana	1,264
15	Arkansas	1,259
16	Arizona	1,245
17	New Mexico	1,228
18	District of Columbia	1,129
19	South Carolina	1,065
20	Nevada	1,041
21	Tennessee	1,039
22	Florida	1,028
23	Georgia	1,000
24	Maryland	996
25	Alabama	994
26	Kansas	994
27	Texas	989
28	Minnesota	975
29	Missouri	967
30	Delaware	962
31	Montana	912
32	Wisconsin	905
33	Nebraska	886
34	Colorado	870
35	Idaho	818
36	Ohio	782
37	West Virginia	778
38	Wyoming	756
39	Kentucky	686
40	California	683
41	North Carolina	667
42	Oklahoma	644
43	Virginia	601
44	New Hampshire	574
45	Puerto Rico	486
46	Washington	467
47	Utah	407
48	Oregon	359
49	Alaska	285
50	Maine	267
51	Vermont	230
52	Hawaii	202

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Rhode Island	24
2	Connecticut	16
3	Michigan	12
4	South Carolina	12
5	Tennessee	12
6	Illinois	11
7	Massachusetts	11
8	New Mexico	11
9	Arkansas	9
10	Iowa	9
11	South Dakota	9
12	Kentucky	8
13	Indiana	7
14	Minnesota	7
15	Mississippi	7
16	New York	7
17	California	6
18	Florida	6
19	Louisiana	6
20	Nevada	6
21	West Virginia	6
22	Alaska	5
23	New Hampshire	5
24	North Carolina	5
25	Ohio	5
26	Oklahoma	5
27	Colorado	4
28	District of Columbia	4
29	Idaho	4
30	Maryland	4
31	Montana	4
32	Vermont	4
33	Maine	3
34	Nebraska	3
35	New Jersey	3
36	Pennsylvania	3
37	Puerto Rico	3
38	Utah	3
39	Arizona	2
40	Delaware	2
41	Kansas	2
42	Texas	2
43	Missouri	1
44	North Dakota	1
45	Oregon	1
46	Virginia	1
47	Washington	1
48	Alabama	0
49	Georgia	0
50	Hawaii	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	273,882	1	99
Dewey	South Dakota	221,656	2	99
Norton	Kansas	221,227	3	99
Lincoln	Arkansas	218,673	4	99
Bon Homme	South Dakota	214,317	5	99
Davidson	Tennessee	103,136	289	90
Richland	South Carolina	66,005	1578	49
York	South Carolina	57,969	1985	36
Orange	California	56,802	2041	35
Pierce	Washington	31,736	2837	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,213	4	99
Iron	Wisconsin	6,154	5	99
Davidson	Tennessee	860	1786	43
Richland	South Carolina	825	1846	41
York	South Carolina	662	2121	32
Orange	California	606	2219	29
Pierce	Washington	397	2590	17

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.